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1.
Aust J Gen Pract ; 53(6): 358-362, 2024 06.
Artigo em Inglês | MEDLINE | ID: mdl-38840373

RESUMO

BACKGROUND: Obstructive sleep apnoea (OSA) is a highly prevalent condition associated with significant adverse health consequences affecting multiple organ systems. As the first point of contact for most patients with OSA, general practitioners (GPs) have an important role in the diagnosis of this common sleep disorder. OBJECTIVE: The aim of this paper is to improve awareness of common risk factors for and clinical presentation of OSA in primary care to improve patient health outcomes. We seek to understand how screening tools, such as the OSA50 questionnaire and the Epworth Sleepiness Scale, can help GPs identify patients who are at high risk for OSA with significant daytime sleepiness. DISCUSSION: Patients at high risk of symptomatic moderate-severe OSA should proceed to further investigation with sleep study testing. Referral to a sleep physician should be considered for patients with complex presentations or other suspected sleep disorders, commercial drivers, and those who fail to comply with or respond to initial OSA treatments.


Assuntos
Atenção Primária à Saúde , Apneia Obstrutiva do Sono , Humanos , Apneia Obstrutiva do Sono/diagnóstico , Apneia Obstrutiva do Sono/complicações , Apneia Obstrutiva do Sono/fisiopatologia , Fatores de Risco , Inquéritos e Questionários , Polissonografia/métodos
3.
Int Heart J ; 65(3): 404-413, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38825490

RESUMO

This study aimed to clarify (1) the association among the atrial fibrillation (AF) type, sleep-disordered breathing (SDB), heart failure (HF), and left atrial (LA) enlargement, (2) the independent predictors of LA enlargement, and (3) the effects of ablation on those conditions in patients with AF. The study's endpoint was LA enlargement (LA volume index [LAVI] ≥ 78 mL/m2).Of 423 patients with nonvalvular AF, 236 were enrolled. We evaluated the role of the clinical parameters such as the AF type, SDB severity, and HF in LA enlargement. Among them, 141 patients exhibiting a 3% oxygen desaturation index (ODI) of ≥ 10 events/hour underwent polysomnography to evaluate the SDB severity measured by the apnea-hypopnea index (AHI). The LA enlargement and HF were characterized by the LA diameter/LAVI, an increase in the B-type natriuretic peptide level, and a lower left ventricular ejection fraction.This study showed that non-paroxysmal AF (NPAF) rather than paroxysmal AF (PAF), the SDB severity, LA enlargement, and HF progression had bidirectional associations and exacerbated each other, which generated a vicious cycle that contributed to the LA enlargement. NPAF (OR = 4.55, P < 0.001), an AHI of ≥ 25.10 events/hour (OR = 1.55, P = 0.003), and a 3% ODI of ≥ 15.43 events/hour (OR = 1.52, P = 0.003) were independent predictors of an acceleration of the LA enlargement. AF ablation improved the HF and LA enlargement.To break this vicious cycle, AF ablation may be the basis for suppressing the LA enlargement and HF progression subsequently eliminating the substrates for AF and SDB in patients with AF.


Assuntos
Fibrilação Atrial , Progressão da Doença , Átrios do Coração , Insuficiência Cardíaca , Índice de Gravidade de Doença , Síndromes da Apneia do Sono , Humanos , Fibrilação Atrial/fisiopatologia , Fibrilação Atrial/complicações , Masculino , Feminino , Síndromes da Apneia do Sono/complicações , Síndromes da Apneia do Sono/fisiopatologia , Síndromes da Apneia do Sono/diagnóstico , Insuficiência Cardíaca/fisiopatologia , Insuficiência Cardíaca/complicações , Pessoa de Meia-Idade , Idoso , Átrios do Coração/fisiopatologia , Átrios do Coração/diagnóstico por imagem , Átrios do Coração/patologia , Ablação por Cateter/métodos , Polissonografia , Remodelamento Atrial/fisiologia , Ecocardiografia
4.
Brain Behav ; 14(6): e3546, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38844423

RESUMO

OBJECTIVE: Rapid eye movement (REM)-dependent obstructive sleep apnea syndrome (OSAS) is a specific subtype of OSAS having some phenotypic characteristics like a preference for a younger age, female gender, and milder severity. Such favorable features could make it possible to consider an overall benign course for this phenotype. However, accumulating data introduced its association with several cardiometabolic and vascular disorders recently. The primary objective of this study was to address the disease from the inflammation perspective and evaluate the potential inflammatory status in this variant via two accessible blood parameters: platelet distribution width (PDW) and systemic immune-inflammation index (SII). The secondary aim was to investigate whether this status, together with other disease characteristics, demonstrates consistency under different definitions of REM-dependent OSAS published previously. PATIENTS AND METHODS: The medical records of 35 patients with mild-to-moderate REM-dependent OSAS, 35 age- and sex-matched patients with REM-independent OSAS, and 25 non-OSA controls were retrospectively analyzed. Baseline features, polysomnographic characteristics, PDW, and SII were compared between the groups. Secondly, the analyses were repeated using different definitions of REM-dependent OSAS. Bivariate analyses were performed, and a multiple stepwise regression model was applied to adjust for body mass index (BMI) and cardiovascular risk (CVR) factors.  RESULTS: Mean PDW and SII were increased in patients with REM-dependent OSAS as compared to non-OSA controls (p = .022 and .029). The significance remained stable after adjustment for BMI and CVRs and was consistent according to different definitions. The Comparison of patients with REM-independent OSAS and non-OSA controls, as well as the two different subtypes of OSAS, did not yield significance. CONCLUSION: Based on the current findings, patients with REM-dependent OSAS appear to be susceptible to inflammation and should be carefully monitored for the negative consequences of that issue. To our knowledge, this study is the first to evaluate SII and PDW in REM-dependent OSAS.


Assuntos
Inflamação , Apneia Obstrutiva do Sono , Humanos , Apneia Obstrutiva do Sono/fisiopatologia , Apneia Obstrutiva do Sono/sangue , Masculino , Feminino , Pessoa de Meia-Idade , Inflamação/sangue , Inflamação/fisiopatologia , Adulto , Estudos Retrospectivos , Sono REM/fisiologia , Polissonografia , Idoso , Índice de Massa Corporal
5.
Transl Psychiatry ; 14(1): 238, 2024 Jun 04.
Artigo em Inglês | MEDLINE | ID: mdl-38834540

RESUMO

The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in arousal-related sleep metrics between 36 individuals with treatment-resistant major depression (TRD) and 25 healthy volunteers (HVs). It also sought to determine whether ketamine normalizes arousal in individuals with TRD and whether ketamine's effects on arousal mediate its antidepressant and anti-SI effects. This was a secondary analysis of a biomarker-focused, randomized, double-blind, crossover trial of ketamine (0.5 mg/kg) compared to saline placebo. Polysomnography (PSG) studies were conducted one day before and one day after ketamine/placebo infusions. Sleep arousal was measured using spectral power functions over time including alpha (quiet wakefulness), beta (alert wakefulness), and delta (deep sleep) power, as well as macroarchitecture variables, including wakefulness after sleep onset (WASO), total sleep time (TST), rapid eye movement (REM) latency, and Post-Sleep Onset Sleep Efficiency (PSOSE). At baseline, diagnostic differences in sleep macroarchitecture included lower TST (p = 0.006) and shorter REM latency (p = 0.04) in the TRD versus HV group. Ketamine's temporal dynamic effects (relative to placebo) in TRD included increased delta power earlier in the night and increased alpha and delta power later in the night. However, there were no significant diagnostic differences in temporal patterns of alpha, beta, or delta power, no ketamine effects on sleep macroarchitecture arousal metrics, and no mediation effects of sleep variables on ketamine's antidepressant or anti-SI effects. These results highlight the role of sleep-related variables as part of the systemic neurobiological changes initiated after ketamine administration. Clinical Trials Identifier: NCT00088699.


Assuntos
Nível de Alerta , Estudos Cross-Over , Transtorno Depressivo Resistente a Tratamento , Ketamina , Polissonografia , Humanos , Ketamina/administração & dosagem , Ketamina/farmacologia , Masculino , Transtorno Depressivo Resistente a Tratamento/tratamento farmacológico , Transtorno Depressivo Resistente a Tratamento/fisiopatologia , Feminino , Adulto , Método Duplo-Cego , Nível de Alerta/efeitos dos fármacos , Pessoa de Meia-Idade , Sono/efeitos dos fármacos , Transtorno Depressivo Maior/tratamento farmacológico , Transtorno Depressivo Maior/fisiopatologia , Vigília/efeitos dos fármacos , Ideação Suicida , Antidepressivos/administração & dosagem , Antidepressivos/farmacologia , Antidepressivos/uso terapêutico , Adulto Jovem
7.
Zhonghua Jie He He Hu Xi Za Zhi ; 47(6): 529-534, 2024 Jun 12.
Artigo em Chinês | MEDLINE | ID: mdl-38858202

RESUMO

Objective: To evaluate the application value of portable pulse oximeter in adult obstructive sleep apnea (OSA). Methods: This study prospectively enrolled adult patients who underwent polysomnography (PSG) due to snoring at the Respiratory and Sleep Medicine Department of Peking University People's Hospital from July 2022 to July 2023. During PSG monitoring, CS-WOxi was continuously used to monitor blood oxygen levels. The consistency between 3% oxygen desaturation index (ODI3) measured by portable pulse oximeter and ODI3 of polysomnography was evaluated using difference test, Pearson's correlation coefficient, and Bland-altman method. Receiver operating characteristic curve was used to determine the optimal threshold for diagnosing OSA. Results: A total of 184 subjects were included, including 121 males (65.8%) and 63 females (34.2%). The mean age was 46.0 (34.3, 59.0) years, body mass index was 26.0 (23.3, 29.6) kg/m², and the apnea-hypopnea index was 18.2 (5.8, 40.8) events/h. There was a significant difference between CS-ODI3 and PSG-ODI3 [17.1(6.2, 42.7) vs. 14.0(2.9, 32.6), P<0.001], and the Pearson correlation coefficient was 0.93 (P<0.001). There was a good correlation between CS-ODI3 and PSG-AHI (r=0.92, P<0.001). Bland-Altman consistency test showed that the average difference between the two was 0.7 events/h, and the 95% consistency limit was (-17.9, 19.3 events/h). When the CS-ODI3≥5 events/h was used to identify OSA, the sensitivity was 94.4%, the specificity was 80.0%, and the accuracy was 91.3%. When PSG-AHI≥5 events/h was used as the diagnostic criteria, the area under the receiver operating characteristic curve was 0.933. Conclusion: Portable pulse oximeter can monitor pulse oxygen saturation accurately and has good sensitivity and specificity for OSA high-risk patients, and is a reliable tool for OSA screening.


Assuntos
Oximetria , Polissonografia , Apneia Obstrutiva do Sono , Humanos , Apneia Obstrutiva do Sono/diagnóstico , Apneia Obstrutiva do Sono/sangue , Oximetria/métodos , Oximetria/instrumentação , Feminino , Masculino , Pessoa de Meia-Idade , Adulto , Polissonografia/métodos , Estudos Prospectivos , Curva ROC , Sensibilidade e Especificidade , Índice de Massa Corporal , Oxigênio/sangue
8.
Zhonghua Jie He He Hu Xi Za Zhi ; 47(6): 554-559, 2024 Jun 12.
Artigo em Chinês | MEDLINE | ID: mdl-38858207

RESUMO

Obstructive sleep apnea (OSA) is primarily characterized by intermittent nocturnal hypoxia and sleep fragmentation. Arousals interrupt sleep continuity and lead to sleep fragmentation, which can lead to cognitive dysfunction, excessive daytime sleepiness, and adverse cardiovascular outcome events, making arousals important for diagnosing OSA and reducing the risk of complications, including heart disease and cognitive impairment. Traditional arousal interpretation requires sleep specialists to manually score PSG recordings throughout the night, which is time consuming and has low inter-specialist agreement, so the search for simple, efficient, and reliable arousal detection methods can be a powerful tool to clinicians. In this paper, we systematically reviewed different methods for recognizing arousal in OSA patients, including autonomic markers (pulse conduction time, pulse wave amplitude, peripheral arterial tone, heart rate, etc.) and machine learning-based automated arousal detection systems, and found that autonomic markers may be more beneficial in certain subgroups, and that deep artificial networks will remain the main research method for automated arousal detection in the future.


Assuntos
Nível de Alerta , Polissonografia , Apneia Obstrutiva do Sono , Humanos , Apneia Obstrutiva do Sono/diagnóstico , Apneia Obstrutiva do Sono/fisiopatologia , Nível de Alerta/fisiologia , Polissonografia/métodos , Aprendizado de Máquina
9.
BMC Health Serv Res ; 24(1): 706, 2024 Jun 05.
Artigo em Inglês | MEDLINE | ID: mdl-38840121

RESUMO

BACKGROUND: Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS. MATERIALS AND METHODS: Clinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938. CONCLUSION: We established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options.


Assuntos
Aprendizado de Máquina , Apneia Obstrutiva do Sono , Humanos , Apneia Obstrutiva do Sono/diagnóstico , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Estudos Retrospectivos , Fatores de Risco , Medição de Risco/métodos , Polissonografia
10.
Artigo em Inglês | MEDLINE | ID: mdl-38848223

RESUMO

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorders.


Assuntos
Algoritmos , Aprendizado Profundo , Eletroencefalografia , Eletroculografia , Redes Neurais de Computação , Fases do Sono , Humanos , Eletroencefalografia/métodos , Fases do Sono/fisiologia , Eletroculografia/métodos , Masculino , Feminino , Adulto , Polissonografia/métodos , Processamento de Sinais Assistido por Computador , Adulto Jovem
11.
Zhongguo Yi Liao Qi Xie Za Zhi ; 48(3): 306-311, 2024 May 30.
Artigo em Chinês | MEDLINE | ID: mdl-38863098

RESUMO

The study provides an overview of the development status of sleep disorder monitoring devices. Currently, polysomnography (PSG) is the gold standard for diagnosing sleep disorders, necessitating multiple leads and requiring overnight monitoring in a sleep laboratory, which can be cumbersome for patients. Nevertheless, the performance of PSG has been enhanced through research on sleep disorder monitoring and sleep staging optimization. An alternative device is the home sleep apnea testing (HSAT), which enables patients to monitor their sleep at home. However, HSAT does not attain the same level of accuracy in sleep staging as PSG, rendering it inappropriate for screening individuals with asymptomatic or mild obstructive sleep apnea-hypopnea syndrome (OSAHS). The study suggests that establishing a Chinese sleep staging database and developing home sleep disorder monitoring devices that can serve as alternatives to PSG will represent a future development direction.


Assuntos
Polissonografia , Apneia Obstrutiva do Sono , Humanos , Monitorização Fisiológica , Monitorização Ambulatorial/instrumentação , Fases do Sono
12.
Vet Q ; 44(1): 1-9, 2024 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38698657

RESUMO

Neurodegenerative diseases are characterised by neuronal loss and abnormal deposition of pathological proteins in the nervous system. Among the most common neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease and transmissible spongiform encephalopathies (TSEs). Sleep and circadian rhythm disturbances are one of the most common symptoms in patients with neurodegenerative diseases. Currently, one of the main objectives in the study of TSEs is to try to establish an early diagnosis, as clinical signs do not appear until the damage to the central nervous system is very advanced, which prevents any therapeutic approach. In this paper, we provide the first description of sleep disturbance caused by classical scrapie in clinical and preclinical sheep using polysomnography compared to healthy controls. Fifteen sheep classified into three groups, clinical, preclinical and negative control, were analysed. The results show a decrease in total sleep time as the disease progresses, with significant changes between control, clinical and pre-clinical animals. The results also show an increase in sleep fragmentation in clinical animals compared to preclinical and control animals. In addition, sheep with clinical scrapie show a total loss of Rapid Eye Movement sleep (REM) and alterations in Non Rapid Eyes Movement sleep (NREM) compared to control sheep, demonstrating more shallow sleep. Although further research is needed, these results suggest that prion diseases also produce sleep disturbances in animals and that polysomnography could be a diagnostic tool of interest in clinical and preclinical cases of prion diseases.


Assuntos
Polissonografia , Scrapie , Transtornos do Sono-Vigília , Animais , Scrapie/diagnóstico , Ovinos , Polissonografia/veterinária , Transtornos do Sono-Vigília/veterinária , Transtornos do Sono-Vigília/diagnóstico , Feminino
13.
Biomed Eng Online ; 23(1): 45, 2024 May 05.
Artigo em Inglês | MEDLINE | ID: mdl-38705982

RESUMO

BACKGROUND: Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep-wake classification and SDB event detection in order to automatically estimate the apnea-hypopnea index (AHI) as severity indicator. METHODS: The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep-wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. RESULTS: We achieved a Cohen's kappa of 0.70 in the sleep-wake classification task, corresponding to a Spearman's correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep-wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen's kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). CONCLUSION: Assisted with automatic sleep-wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep-wake detection.


Assuntos
Respiração , Processamento de Sinais Assistido por Computador , Síndromes da Apneia do Sono , Síndromes da Apneia do Sono/fisiopatologia , Síndromes da Apneia do Sono/diagnóstico , Humanos , Masculino , Pessoa de Meia-Idade , Polissonografia , Feminino , Aprendizado de Máquina , Adulto , Redes Neurais de Computação , Eletrocardiografia , Idoso , Vigília/fisiologia , Sono
14.
Artigo em Inglês | MEDLINE | ID: mdl-38696294

RESUMO

To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep learning-based model using a 30 sec sleep electroencephalogram (EEG) early in the sleep cycle to predict sleep onset latency (SOL) distribution and explore associations with sleep quality (SQ). We propose a deep learning model composed of a structure that decomposes and restores the signal in epoch units and a structure that predicts the SOL distribution. We used the Sleep Heart Health Study public dataset, which includes a large number of study subjects, to estimate and evaluate the proposed model. The proposed model estimated the SOL distribution and divided it into four clusters. The advantage of the proposed model is that it shows the process of falling asleep for individual participants as a probability graph over time. Furthermore, we compared the baseline of good SQ and SOL and showed that less than 10 minutes SOL correlated better with good SQ. Moreover, it was the most suitable sleep feature that could be predicted using early EEG, compared with the total sleep time, sleep efficiency, and actual sleep time. Our study showed the feasibility of estimating SOL distribution using deep learning with an early EEG and showed that SOL distribution within 10 minutes was associated with good SQ.


Assuntos
Aprendizado Profundo , Eletroencefalografia , Qualidade do Sono , Humanos , Masculino , Feminino , Adulto , Latência do Sono/fisiologia , Pessoa de Meia-Idade , Algoritmos , Idoso , Polissonografia , Sono/fisiologia
15.
Respir Res ; 25(1): 197, 2024 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-38715026

RESUMO

BACKGROUND AND OBJECTIVES: OSA is a known medical condition that is associated with several comorbidities and affect patients' quality of life. The association between OSA and lung cancer remains debated. Some studies reported increased prevalence of OSA in patients with lung cancer. We aimed to assess predictors of moderate-to-severe OSA in patients with lung cancer. METHODS: We enrolled 153 adult patients who were newly diagnosed with lung cancer. Cardiorespiratory monitoring was performed using home sleep apnea device. We carried out Univariate and multivariate logistic regression analysis on multiple parameters including age, gender, smoking status, neck circumference, waist circumference, BMI, stage and histopathology of lung cancer, presence of superior vena cava obstruction, and performance status to find out the factors that are independently associated with a diagnosis of moderate-to-severe OSA. RESULTS: Our results suggest that poor performance status is the most significant predictor of moderate to severe OSA in patients with lung cancer after controlling for important confounders. CONCLUSION: Performance status is a predictor of moderate to severe OSA in patients with lung cancer in our population of middle eastern ethnicity.


Assuntos
Neoplasias Pulmonares , Índice de Gravidade de Doença , Apneia Obstrutiva do Sono , Humanos , Masculino , Feminino , Apneia Obstrutiva do Sono/epidemiologia , Apneia Obstrutiva do Sono/diagnóstico , Apneia Obstrutiva do Sono/fisiopatologia , Pessoa de Meia-Idade , Neoplasias Pulmonares/epidemiologia , Neoplasias Pulmonares/diagnóstico , Idoso , Valor Preditivo dos Testes , Adulto , Fatores de Risco , Polissonografia/métodos
16.
Respir Res ; 25(1): 214, 2024 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-38762509

RESUMO

OBJECTIVES: Obstructive sleep apnea (OSA) is associated with abnormal glucose and lipid metabolism. However, whether there is an independent association between Sleep Apnea-Specific Hypoxic Burden (SASHB) and glycolipid metabolism disorders in patients with OSA is unknown. METHODS: We enrolled 2,173 participants with suspected OSA from January 2019 to July 2023 in this study. Polysomnographic variables, biochemical indicators, and physical measurements were collected from each participant. Multiple linear regression analyses were used to evaluate independent associations between SASHB, AHI, CT90 and glucose as well as lipid profile. Furthermore, logistic regressions were used to determine the odds ratios (ORs) for abnormal glucose and lipid metabolism across various SASHB, AHI, CT90 quartiles. RESULTS: The SASHB was independently associated with fasting blood glucose (FBG) (ß = 0.058, P = 0.016), fasting insulin (FIN) (ß = 0.073, P < 0.001), homeostasis model assessment of insulin resistance (HOMA-IR) (ß = 0.058, P = 0.011), total cholesterol (TC) (ß = 0.100, P < 0.001), total triglycerides (TG) (ß = 0.063, P = 0.011), low-density lipoprotein cholesterol (LDL-C) (ß = 0.075, P = 0.003), apolipoprotein A-I (apoA-I) (ß = 0.051, P = 0.049), apolipoprotein B (apoB) (ß = 0.136, P < 0.001), apolipoprotein E (apoE) (ß = 0.088, P < 0.001) after adjustments for confounding factors. Furthermore, the ORs for hyperinsulinemia across the higher SASHB quartiles were 1.527, 1.545, and 2.024 respectively, compared with the lowest quartile (P < 0.001 for a linear trend); the ORs for hyper-total cholesterolemia across the higher SASHB quartiles were 1.762, 1.998, and 2.708, compared with the lowest quartile (P < 0.001 for a linear trend) and the ORs for hyper-LDL cholesterolemia across the higher SASHB quartiles were 1.663, 1.695, and 2.316, compared with the lowest quartile (P < 0.001 for a linear trend). Notably, the ORs for hyper-triglyceridemia{1.471, 1.773, 2.099} and abnormal HOMA-IR{1.510, 1.492, 1.937} maintained a consistent trend across the SASHB quartiles. CONCLUSIONS: We found SASHB was independently associated with hyperinsulinemia, abnormal HOMA-IR, hyper-total cholesterolemia, hyper-triglyceridemia and hyper-LDL cholesterolemia in Chinese Han population. Further prospective studies are needed to confirm that SASHB can be used as a predictor of abnormal glycolipid metabolism disorders in patients with OSA. TRIAL REGISTRATION: ChiCTR1900025714 { http://www.chictr.org.cn/ }; Prospectively registered on 6 September 2019; China.


Assuntos
Hipóxia , Apneia Obstrutiva do Sono , Humanos , Masculino , Feminino , Estudos Transversais , Pessoa de Meia-Idade , Adulto , Hipóxia/sangue , Hipóxia/epidemiologia , Apneia Obstrutiva do Sono/epidemiologia , Apneia Obstrutiva do Sono/sangue , Apneia Obstrutiva do Sono/diagnóstico , Glicemia/metabolismo , Transtornos do Metabolismo dos Lipídeos/epidemiologia , Transtornos do Metabolismo dos Lipídeos/sangue , Transtornos do Metabolismo dos Lipídeos/diagnóstico , Idoso , Polissonografia , Metabolismo dos Lipídeos/fisiologia , Resistência à Insulina/fisiologia
17.
IEEE J Transl Eng Health Med ; 12: 448-456, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38765887

RESUMO

OBJECTIVE: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.


Assuntos
Eletroencefalografia , Couro Cabeludo , Humanos , Eletroencefalografia/métodos , Idoso , Couro Cabeludo/fisiologia , Idoso de 80 Anos ou mais , Masculino , Feminino , Sono/fisiologia , Processamento de Sinais Assistido por Computador , Orelha/fisiologia , Aprendizado de Máquina , Polissonografia/métodos
18.
Respir Med ; 227: 107641, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38710399

RESUMO

BACKGROUND: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. RESEARCH QUESTION: To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. STUDY DESIGN AND METHODS: A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. RESULTS: Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. INTERPRETATION: We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.


Assuntos
Aprendizado de Máquina , Fenótipo , Polissonografia , Doença Pulmonar Obstrutiva Crônica , Transtornos do Sono-Vigília , Humanos , Doença Pulmonar Obstrutiva Crônica/fisiopatologia , Doença Pulmonar Obstrutiva Crônica/mortalidade , Doença Pulmonar Obstrutiva Crônica/complicações , Doença Pulmonar Obstrutiva Crônica/epidemiologia , Análise por Conglomerados , Masculino , Feminino , Idoso , Estudos Longitudinais , Pessoa de Meia-Idade , Transtornos do Sono-Vigília/epidemiologia , Transtornos do Sono-Vigília/fisiopatologia , Polissonografia/métodos , Sono/fisiologia , Comorbidade , Qualidade de Vida , Aprendizado de Máquina não Supervisionado , Fatores Etários , Estudos de Coortes
19.
Am J Case Rep ; 25: e943346, 2024 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-38720444

RESUMO

BACKGROUND Numerous countries, Vietnam included, have persistently high annual rates of traffic accidents. Despite concerted government efforts to reduce the annual traffic accident rate, the toll of fatalities and consequential injuries from these accidents rises each year. Various factors contribute to these incidents, notably including alcohol consumption while driving, inadequate awareness of traffic regulations, and substandard traffic infrastructure. However, an under-recognized risk in developing nations such as Vietnam is the prevalence of sleep disorders. Conditions such as obstructive sleep apnea syndrome and obesity hypoventilation syndrome, while prevalent, remain inadequately assessed and treated. These disorders represent significant yet largely unaddressed contributors to the heightened risk of traffic accidents. CASE REPORT We describe the case of a 55-year-old Vietnamese man hospitalized due to long-standing respiratory complications and profound daytime sleepiness. Over the past 2 years, the patient gained 10 kg. Consequently, he frequently experienced drowsiness, leading to 4 traffic accidents. Despite previous hospitalizations, this sleep disorder had gone undiagnosed and untreated. Diagnostic assessments confirmed concurrent obstructive sleep apnea and obesity hypoventilation syndrome through polysomnography and blood gas analyses. Treatment involving non-invasive positive airway pressure therapy notably alleviated symptoms and substantially improved his quality of life within a concise 3-month period. CONCLUSIONS Obstructive sleep apnea and obesity hypoventilation syndrome are contributory factors to excessive daytime somnolence, significantly increasing vulnerability to traffic accidents. Regrettably, this critical intersection remains inadequately addressed. Addressing these concerns comprehensively through dedicated research initiatives should be imperative before considering the universal issuance of driver's licenses to all road users in Vietnam.


Assuntos
Acidentes de Trânsito , Apneia Obstrutiva do Sono , Humanos , Masculino , Pessoa de Meia-Idade , Apneia Obstrutiva do Sono/epidemiologia , Apneia Obstrutiva do Sono/terapia , Síndrome de Hipoventilação por Obesidade , Vietnã/epidemiologia , Polissonografia
20.
BMC Oral Health ; 24(1): 565, 2024 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-38745301

RESUMO

BACKGROUND: The etiology of sleep bruxism in obstructive sleep apnea (OSA) patients is not yet fully clarified. This prospective clinical study aimed to investigate the connection between probable sleep bruxism, electromyographic muscle tone, and respiratory sleep patterns recorded during polysomnography. METHODS: 106 patients with OSA (74 males, 31 females, mean age: 56.1 ± 11.4 years) were divided into two groups (sleep bruxism: SB; no sleep bruxism: NSB). Probable SB were based on the AASM criteria: self-report of clenching/grinding, orofacial symptoms upon awakening, abnormal tooth wear and hypertrophy of the masseter muscle. Both groups underwent clinical examination for painful muscle symptoms aligned with Temporomandibular Disorders Diagnostic Criteria (DC/TMD), such as myalgia, myofascial pain, and headache attributed to temporomandibular disorder. Additionally, non-complaint positive muscle palpation and orofacial-related limitations (Jaw Functional Limited Scale-20: JFLS-20) were assessed. A one-night polysomnography with electromyographic masseter muscle tone (EMG) measurement was performed. Descriptive data, inter-group comparisons and multivariate logistic regression were calculated. RESULTS: OSA patients had a 37.1% prevalence of SB. EMG muscle tone (N1-N3, REM; P = 0.001) and the number of hypopneas (P = 0.042) were significantly higher in the sleep bruxism group. While measures like apnea-hypopnea-index (AHI), respiratory-disturbance-index (RDI), apnea index (AI), hypopnea-index (HI), number of arousals, and heart rate (1/min) were elevated in sleep bruxers, the differences were not statistically significant. There was no difference in sleep efficiency (SE; P = 0.403). Non-complaint masseter muscle palpation (61.5%; P = 0.015) and myalgia (41%; P = 0.010) were significant higher in SB patients. Multivariate logistic regression showed a significant contribution of EMG muscle tone and JFLS-20 to bruxism risk. CONCLUSION: Increased EMG muscle tone and orofacial limitations can predict sleep bruxism in OSA patients. Besides, SB patients suffer more from sleep disorder breathing. Thus, sleep bruxism seems to be not only an oral health related problem in obstructive apnea. Consequently, interdisciplinary interventions are crucial for effectively treating these patients. TRIAL REGISTRATION: The study was approved by the Ethics Committee of Philipps-University Marburg (reg. no. 13/22-2022) and registered at the "German Clinical Trial Register, DRKS" (DRKS0002959).


Assuntos
Eletromiografia , Polissonografia , Apneia Obstrutiva do Sono , Bruxismo do Sono , Humanos , Masculino , Feminino , Apneia Obstrutiva do Sono/fisiopatologia , Apneia Obstrutiva do Sono/complicações , Bruxismo do Sono/complicações , Bruxismo do Sono/fisiopatologia , Pessoa de Meia-Idade , Estudos Prospectivos , Músculo Masseter/fisiopatologia , Saúde Bucal , Adulto , Tono Muscular/fisiologia
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